Exercise metabolism during moderate-intensity exercise in children with cystic fibrosis following heavy-intensity exercise
Bibliographic record
Abstract
Muscle metabolism is increased following exercise in healthy individuals, affecting exercise metabolism during subsequent physical work. We hypothesized that following heavy-intensity exercise (HIE), disease factors in children with cystic fibrosis (CF) would further exacerbate exercise metabolism and perceived exertion during subsequent exercise. Nineteen children with CF (age, 13.4 ± 3.1 years; 10 female) and 19 healthy controls (age, 13.8 ± 3.5 years; 10 female) performed 10 bouts of HIE interspersed with 1 min of recovery between each bout. Three minutes later participants completed a 10-min moderate-intensity exercise (MIE) test (test 1). The MIE test was subsequently repeated 1 h (test 2) and 24 h (test 3) later. Each MIE test was identical and participants exercised at individualized work rates, calibrated by an initial graded maximal cardiopulmonary exercise test, while metabolic and perceived exertion measurements were taken. Following HIE, mixed-model ANOVAs showed a significant difference in oxygen uptake (VO₂) and rating of perceived exertion (RPE) between the 2 groups across the MIE tests (p < 0.01). In controls, VO₂ (L·min⁻¹) and RPE decreased significantly from test 1 to test 2 (p < 0.01) and test 2 to test 3 (p < 0.05). However, in children with CF, VO₂ (L·min⁻¹) increased significantly from test 1 to test 2 (p < 0.01), while RPE did not differ, both VO₂ and RPE decreased significantly from test 2 to test 3 (p < 0.01). In conclusion, following HIE the metabolic and perceptual responses to MIE in both groups decreased 24 h later during test 3. These data show that children with mild-to-moderate CF have the capability to perform HIE and 24 h allows sufficient time for recovery.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".